Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations1223203
Missing cells17751
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory759.0 MiB
Average record size in memory650.7 B

Variable types

Text5
Numeric15
DateTime2
Boolean1
Categorical2

Alerts

energy is highly overall correlated with loudnessHigh correlation
loudness is highly overall correlated with energyHigh correlation
tempo is highly overall correlated with time_signatureHigh correlation
time_signature is highly overall correlated with tempoHigh correlation
time_signature is highly imbalanced (76.1%)Imbalance
country has 16607 (1.4%) missing valuesMissing
daily_movement has 317147 (25.9%) zerosZeros
weekly_movement has 138892 (11.4%) zerosZeros
key has 105243 (8.6%) zerosZeros
instrumentalness has 589443 (48.2%) zerosZeros

Reproduction

Analysis started2024-09-24 17:40:27.907066
Analysis finished2024-09-24 17:44:08.765697
Duration3 minutes and 40.86 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Distinct15142
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size92.2 MiB
2024-09-24T12:44:08.989815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters26910466
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2975 ?
Unique (%)0.2%

Sample

1st row2plbrEY59IikOBgBGLjaoe
2nd row6dOtVTDdiauQNBQEDOtlAB
3rd row5G2f63n7IPVPPjfNIGih7Q
4th row7tI8dRuH2Yc6RuoTjxo4dU
5th row2qSkIjg1o9h3YT9RAgYN75
ValueCountFrequency (%)
3rugc1vupkdg9czfhmur1t 8461
 
0.7%
17phhzdn6ogtzme56nuwvj 7622
 
0.6%
2qskijg1o9h3yt9ragyn75 7448
 
0.6%
1bxfupkguatgp7am0bbdwr 6452
 
0.5%
6dotvtddiauqnbqedotlab 6116
 
0.5%
6xjdf6nds4de2bbbagzol6 5910
 
0.5%
3xkhsmpqcbmytmjnidf3ii 5659
 
0.5%
5rqssdztnpx1uzkmlhcfvk 5654
 
0.5%
7iqxytyug13aoehxgg28nh 5510
 
0.5%
7bywjhoc0wsjggbj04xbvi 5367
 
0.4%
Other values (15132) 1159004
94.8%
2024-09-24T12:44:09.497313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 626908
 
2.3%
0 591197
 
2.2%
3 581624
 
2.2%
5 564989
 
2.1%
7 557864
 
2.1%
2 541096
 
2.0%
4 536787
 
2.0%
6 531024
 
2.0%
j 473809
 
1.8%
Q 464521
 
1.7%
Other values (52) 21440647
79.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10797830
40.1%
Lowercase Letter 10782958
40.1%
Decimal Number 5329678
19.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
j 473809
 
4.4%
m 454031
 
4.2%
t 453315
 
4.2%
b 433523
 
4.0%
o 430239
 
4.0%
u 429750
 
4.0%
p 427237
 
4.0%
y 426542
 
4.0%
i 426096
 
4.0%
d 422376
 
3.9%
Other values (16) 6406040
59.4%
Uppercase Letter
ValueCountFrequency (%)
Q 464521
 
4.3%
C 444762
 
4.1%
D 435827
 
4.0%
F 434607
 
4.0%
R 433556
 
4.0%
I 431989
 
4.0%
H 430811
 
4.0%
Z 430189
 
4.0%
N 428825
 
4.0%
B 428465
 
4.0%
Other values (16) 6434278
59.6%
Decimal Number
ValueCountFrequency (%)
1 626908
11.8%
0 591197
11.1%
3 581624
10.9%
5 564989
10.6%
7 557864
10.5%
2 541096
10.2%
4 536787
10.1%
6 531024
10.0%
8 404121
7.6%
9 394068
7.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 21580788
80.2%
Common 5329678
 
19.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
j 473809
 
2.2%
Q 464521
 
2.2%
m 454031
 
2.1%
t 453315
 
2.1%
C 444762
 
2.1%
D 435827
 
2.0%
F 434607
 
2.0%
R 433556
 
2.0%
b 433523
 
2.0%
I 431989
 
2.0%
Other values (42) 17120848
79.3%
Common
ValueCountFrequency (%)
1 626908
11.8%
0 591197
11.1%
3 581624
10.9%
5 564989
10.6%
7 557864
10.5%
2 541096
10.2%
4 536787
10.1%
6 531024
10.0%
8 404121
7.6%
9 394068
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26910466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 626908
 
2.3%
0 591197
 
2.2%
3 581624
 
2.2%
5 564989
 
2.1%
7 557864
 
2.1%
2 541096
 
2.0%
4 536787
 
2.0%
6 531024
 
2.0%
j 473809
 
1.8%
Q 464521
 
1.7%
Other values (52) 21440647
79.7%

name
Text

Distinct13652
Distinct (%)1.1%
Missing27
Missing (%)< 0.1%
Memory size94.5 MiB
2024-09-24T12:44:09.923089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length286
Median length107
Mean length14.352787
Min length1

Characters and Unicode

Total characters17555984
Distinct characters1218
Distinct categories21 ?
Distinct scripts18 ?
Distinct blocks26 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2656 ?
Unique (%)0.2%

Sample

1st rowDie With A Smile
2nd rowBIRDS OF A FEATHER
3rd rowTaste
4th rowWho
5th rowEspresso
ValueCountFrequency (%)
88826
 
2.7%
feat 72492
 
2.2%
the 56726
 
1.7%
la 33182
 
1.0%
me 31738
 
1.0%
i 28120
 
0.9%
of 25611
 
0.8%
you 25337
 
0.8%
love 25230
 
0.8%
with 24155
 
0.7%
Other values (16779) 2855808
87.4%
2024-09-24T12:44:10.646090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2044049
 
11.6%
e 1190208
 
6.8%
a 1103106
 
6.3%
i 812448
 
4.6%
o 792271
 
4.5%
n 687800
 
3.9%
t 651996
 
3.7%
r 599378
 
3.4%
l 477901
 
2.7%
s 470613
 
2.7%
Other values (1208) 8726214
49.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9772981
55.7%
Uppercase Letter 4365443
24.9%
Space Separator 2044049
 
11.6%
Other Letter 561263
 
3.2%
Other Punctuation 275603
 
1.6%
Close Punctuation 155239
 
0.9%
Open Punctuation 154904
 
0.9%
Decimal Number 126070
 
0.7%
Dash Punctuation 60211
 
0.3%
Nonspacing Mark 21036
 
0.1%
Other values (11) 19185
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 33350
 
5.9%
ي 29357
 
5.2%
ل 19007
 
3.4%
م 16379
 
2.9%
ن 15052
 
2.7%
י 15009
 
2.7%
ו 13394
 
2.4%
و 12791
 
2.3%
ب 11188
 
2.0%
ت 9632
 
1.7%
Other values (761) 386104
68.8%
Lowercase Letter
ValueCountFrequency (%)
e 1190208
12.2%
a 1103106
11.3%
i 812448
 
8.3%
o 792271
 
8.1%
n 687800
 
7.0%
t 651996
 
6.7%
r 599378
 
6.1%
l 477901
 
4.9%
s 470613
 
4.8%
u 346007
 
3.5%
Other values (181) 2641253
27.0%
Uppercase Letter
ValueCountFrequency (%)
A 383572
 
8.8%
S 333041
 
7.6%
L 290905
 
6.7%
E 288372
 
6.6%
T 257025
 
5.9%
M 238263
 
5.5%
O 235739
 
5.4%
N 230930
 
5.3%
R 224354
 
5.1%
I 220092
 
5.0%
Other values (137) 1663150
38.1%
Other Punctuation
ValueCountFrequency (%)
. 105486
38.3%
' 41059
 
14.9%
, 33693
 
12.2%
" 24278
 
8.8%
& 22867
 
8.3%
! 16168
 
5.9%
? 14842
 
5.4%
: 5374
 
1.9%
/ 4250
 
1.5%
# 3798
 
1.4%
Other values (16) 3788
 
1.4%
Nonspacing Mark
ValueCountFrequency (%)
5169
24.6%
3949
18.8%
3413
16.2%
3245
15.4%
1762
 
8.4%
934
 
4.4%
687
 
3.3%
554
 
2.6%
355
 
1.7%
271
 
1.3%
Other values (9) 697
 
3.3%
Other Symbol
ValueCountFrequency (%)
56
14.0%
🇩 50
12.5%
🇪 50
12.5%
® 42
10.5%
40
10.0%
💤 37
9.3%
24
6.0%
🍸 19
 
4.8%
💀 17
 
4.3%
🦚 16
 
4.0%
Other values (9) 48
12.0%
Decimal Number
ValueCountFrequency (%)
0 29893
23.7%
1 28118
22.3%
2 19157
15.2%
3 14347
11.4%
4 9046
 
7.2%
5 8995
 
7.1%
9 5626
 
4.5%
8 4572
 
3.6%
7 4074
 
3.2%
6 2242
 
1.8%
Math Symbol
ValueCountFrequency (%)
| 4393
59.1%
+ 1667
 
22.4%
798
 
10.7%
< 482
 
6.5%
> 73
 
1.0%
= 9
 
0.1%
7
 
0.1%
1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 150519
97.2%
[ 3245
 
2.1%
972
 
0.6%
78
 
0.1%
69
 
< 0.1%
21
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 150923
97.2%
] 3245
 
2.1%
972
 
0.6%
78
 
0.1%
21
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 60167
99.9%
44
 
0.1%
Final Punctuation
ValueCountFrequency (%)
4251
92.1%
363
 
7.9%
Currency Symbol
ValueCountFrequency (%)
$ 3009
99.3%
20
 
0.7%
Modifier Letter
ValueCountFrequency (%)
1239
98.2%
23
 
1.8%
Initial Punctuation
ValueCountFrequency (%)
328
65.2%
175
34.8%
Space Separator
ValueCountFrequency (%)
2044049
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1653
100.0%
Format
ValueCountFrequency (%)
198
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 61
100.0%
Other Number
ValueCountFrequency (%)
21
100.0%
Enclosing Mark
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13819748
78.7%
Common 2835199
 
16.1%
Cyrillic 318537
 
1.8%
Arabic 225357
 
1.3%
Hebrew 118325
 
0.7%
Thai 107967
 
0.6%
Han 98713
 
0.6%
Katakana 21100
 
0.1%
Hiragana 10119
 
0.1%
Inherited 446
 
< 0.1%
Other values (8) 473
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
2606
 
2.6%
2003
 
2.0%
1617
 
1.6%
1553
 
1.6%
1326
 
1.3%
1178
 
1.2%
1164
 
1.2%
1141
 
1.2%
1037
 
1.1%
1032
 
1.0%
Other values (552) 84056
85.2%
Latin
ValueCountFrequency (%)
e 1190208
 
8.6%
a 1103106
 
8.0%
i 812448
 
5.9%
o 792271
 
5.7%
n 687800
 
5.0%
t 651996
 
4.7%
r 599378
 
4.3%
l 477901
 
3.5%
s 470613
 
3.4%
A 383572
 
2.8%
Other values (230) 6650455
48.1%
Common
ValueCountFrequency (%)
2044049
72.1%
) 150923
 
5.3%
( 150519
 
5.3%
. 105486
 
3.7%
- 60167
 
2.1%
' 41059
 
1.4%
, 33693
 
1.2%
0 29893
 
1.1%
1 28118
 
1.0%
" 24278
 
0.9%
Other values (74) 167014
 
5.9%
Cyrillic
ValueCountFrequency (%)
а 29274
 
9.2%
о 23616
 
7.4%
е 23484
 
7.4%
и 17832
 
5.6%
т 17698
 
5.6%
н 13423
 
4.2%
л 13122
 
4.1%
р 13120
 
4.1%
с 9809
 
3.1%
к 9411
 
3.0%
Other values (62) 147748
46.4%
Katakana
ValueCountFrequency (%)
1742
 
8.3%
1601
 
7.6%
1567
 
7.4%
1468
 
7.0%
1046
 
5.0%
1044
 
4.9%
748
 
3.5%
689
 
3.3%
680
 
3.2%
674
 
3.2%
Other values (46) 9841
46.6%
Thai
ValueCountFrequency (%)
7235
 
6.7%
6194
 
5.7%
5641
 
5.2%
5265
 
4.9%
5169
 
4.8%
4895
 
4.5%
4826
 
4.5%
4382
 
4.1%
4234
 
3.9%
3949
 
3.7%
Other values (45) 56177
52.0%
Arabic
ValueCountFrequency (%)
ا 33350
14.8%
ي 29357
13.0%
ل 19007
 
8.4%
م 16379
 
7.3%
ن 15052
 
6.7%
و 12791
 
5.7%
ب 11188
 
5.0%
ت 9632
 
4.3%
ر 9135
 
4.1%
ع 7339
 
3.3%
Other values (24) 62127
27.6%
Hiragana
ValueCountFrequency (%)
973
 
9.6%
872
 
8.6%
724
 
7.2%
677
 
6.7%
530
 
5.2%
480
 
4.7%
462
 
4.6%
461
 
4.6%
404
 
4.0%
402
 
4.0%
Other values (24) 4134
40.9%
Hebrew
ValueCountFrequency (%)
י 15009
12.7%
ו 13394
 
11.3%
ה 8529
 
7.2%
א 8340
 
7.0%
ל 7930
 
6.7%
ת 7013
 
5.9%
ב 6909
 
5.8%
ר 6386
 
5.4%
מ 5003
 
4.2%
ש 4321
 
3.7%
Other values (17) 35491
30.0%
Greek
ValueCountFrequency (%)
Λ 53
21.3%
σ 20
 
8.0%
τ 15
 
6.0%
Ν 12
 
4.8%
Α 12
 
4.8%
ν 10
 
4.0%
ε 10
 
4.0%
ι 10
 
4.0%
ό 10
 
4.0%
π 10
 
4.0%
Other values (16) 87
34.9%
Hangul
ValueCountFrequency (%)
52
29.5%
52
29.5%
52
29.5%
12
 
6.8%
1
 
0.6%
1
 
0.6%
1
 
0.6%
1
 
0.6%
1
 
0.6%
1
 
0.6%
Other values (2) 2
 
1.1%
Inherited
ValueCountFrequency (%)
ّ 185
41.5%
ِ 146
32.7%
85
19.1%
15
 
3.4%
̄ 10
 
2.2%
̧ 3
 
0.7%
ً 1
 
0.2%
̌ 1
 
0.2%
Tibetan
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Malayalam
ValueCountFrequency (%)
21
100.0%
Bengali
ValueCountFrequency (%)
9
100.0%
Coptic
ValueCountFrequency (%)
7
100.0%
Batak
ValueCountFrequency (%)
7
100.0%
Balinese
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16450714
93.7%
Cyrillic 318537
 
1.8%
Arabic 225942
 
1.3%
None 180607
 
1.0%
Hebrew 118325
 
0.7%
Thai 107967
 
0.6%
CJK 98690
 
0.6%
Katakana 22339
 
0.1%
Latin Ext Additional 16540
 
0.1%
Hiragana 10119
 
0.1%
Other values (16) 6204
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2044049
 
12.4%
e 1190208
 
7.2%
a 1103106
 
6.7%
i 812448
 
4.9%
o 792271
 
4.8%
n 687800
 
4.2%
t 651996
 
4.0%
r 599378
 
3.6%
l 477901
 
2.9%
s 470613
 
2.9%
Other values (79) 7620944
46.3%
Arabic
ValueCountFrequency (%)
ا 33350
14.8%
ي 29357
13.0%
ل 19007
 
8.4%
م 16379
 
7.2%
ن 15052
 
6.7%
و 12791
 
5.7%
ب 11188
 
5.0%
ت 9632
 
4.3%
ر 9135
 
4.0%
ع 7339
 
3.2%
Other values (29) 62712
27.8%
Cyrillic
ValueCountFrequency (%)
а 29274
 
9.2%
о 23616
 
7.4%
е 23484
 
7.4%
и 17832
 
5.6%
т 17698
 
5.6%
н 13423
 
4.2%
л 13122
 
4.1%
р 13120
 
4.1%
с 9809
 
3.1%
к 9411
 
3.0%
Other values (62) 147748
46.4%
None
ValueCountFrequency (%)
é 15042
 
8.3%
á 14741
 
8.2%
í 10882
 
6.0%
ó 10503
 
5.8%
ä 8513
 
4.7%
ú 8071
 
4.5%
Ó 7861
 
4.4%
ı 6727
 
3.7%
Í 6182
 
3.4%
ü 4857
 
2.7%
Other values (173) 87228
48.3%
Hebrew
ValueCountFrequency (%)
י 15009
12.7%
ו 13394
 
11.3%
ה 8529
 
7.2%
א 8340
 
7.0%
ל 7930
 
6.7%
ת 7013
 
5.9%
ב 6909
 
5.8%
ר 6386
 
5.4%
מ 5003
 
4.2%
ש 4321
 
3.7%
Other values (17) 35491
30.0%
Thai
ValueCountFrequency (%)
7235
 
6.7%
6194
 
5.7%
5641
 
5.2%
5265
 
4.9%
5169
 
4.8%
4895
 
4.5%
4826
 
4.5%
4382
 
4.1%
4234
 
3.9%
3949
 
3.7%
Other values (45) 56177
52.0%
Punctuation
ValueCountFrequency (%)
4251
75.8%
363
 
6.5%
328
 
5.8%
198
 
3.5%
176
 
3.1%
175
 
3.1%
69
 
1.2%
44
 
0.8%
5
 
0.1%
CJK
ValueCountFrequency (%)
2606
 
2.6%
2003
 
2.0%
1617
 
1.6%
1553
 
1.6%
1326
 
1.3%
1178
 
1.2%
1164
 
1.2%
1141
 
1.2%
1037
 
1.1%
1032
 
1.0%
Other values (551) 84033
85.1%
Katakana
ValueCountFrequency (%)
1742
 
7.8%
1601
 
7.2%
1567
 
7.0%
1468
 
6.6%
1239
 
5.5%
1046
 
4.7%
1044
 
4.7%
748
 
3.3%
689
 
3.1%
680
 
3.0%
Other values (47) 10515
47.1%
Latin Ext Additional
ValueCountFrequency (%)
1265
 
7.6%
1192
 
7.2%
ế 1119
 
6.8%
971
 
5.9%
894
 
5.4%
893
 
5.4%
831
 
5.0%
760
 
4.6%
734
 
4.4%
597
 
3.6%
Other values (45) 7284
44.0%
Hiragana
ValueCountFrequency (%)
973
 
9.6%
872
 
8.6%
724
 
7.2%
677
 
6.7%
530
 
5.2%
480
 
4.7%
462
 
4.6%
461
 
4.6%
404
 
4.0%
402
 
4.0%
Other values (24) 4134
40.9%
VS
ValueCountFrequency (%)
85
100.0%
Misc Symbols
ValueCountFrequency (%)
56
42.1%
40
30.1%
24
18.0%
12
 
9.0%
1
 
0.8%
Hangul
ValueCountFrequency (%)
52
29.5%
52
29.5%
52
29.5%
12
 
6.8%
1
 
0.6%
1
 
0.6%
1
 
0.6%
1
 
0.6%
1
 
0.6%
1
 
0.6%
Other values (2) 2
 
1.1%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🇩 50
50.0%
🇪 50
50.0%
Malayalam
ValueCountFrequency (%)
21
100.0%
Currency Symbols
ValueCountFrequency (%)
20
100.0%
Diacriticals
ValueCountFrequency (%)
̄ 10
71.4%
̧ 3
 
21.4%
̌ 1
 
7.1%
Dingbats
ValueCountFrequency (%)
9
90.0%
1
 
10.0%
Bengali
ValueCountFrequency (%)
9
100.0%
Math Operators
ValueCountFrequency (%)
7
87.5%
1
 
12.5%
Coptic
ValueCountFrequency (%)
7
100.0%
Batak
ValueCountFrequency (%)
7
100.0%
Tibetan
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Misc Technical
ValueCountFrequency (%)
1
100.0%
Balinese
ValueCountFrequency (%)
1
100.0%
Distinct9043
Distinct (%)0.7%
Missing27
Missing (%)< 0.1%
Memory size90.5 MiB
2024-09-24T12:44:11.051539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length284
Median length109
Mean length15.932132
Min length1

Characters and Unicode

Total characters19487801
Distinct characters503
Distinct categories15 ?
Distinct scripts11 ?
Distinct blocks14 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1564 ?
Unique (%)0.1%

Sample

1st rowLady Gaga, Bruno Mars
2nd rowBillie Eilish
3rd rowSabrina Carpenter
4th rowJimin
5th rowSabrina Carpenter
ValueCountFrequency (%)
feid 37175
 
1.1%
the 31656
 
1.0%
bad 27930
 
0.9%
bunny 27024
 
0.8%
g 23535
 
0.7%
swift 21562
 
0.7%
taylor 21094
 
0.6%
karol 20916
 
0.6%
mc 20642
 
0.6%
young 19522
 
0.6%
Other values (10956) 3003861
92.3%
2024-09-24T12:44:11.782147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2031999
 
10.4%
a 1663962
 
8.5%
e 1299824
 
6.7%
i 1115774
 
5.7%
o 988285
 
5.1%
n 978885
 
5.0%
r 900972
 
4.6%
, 740567
 
3.8%
l 703601
 
3.6%
s 533464
 
2.7%
Other values (493) 8530468
43.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12198708
62.6%
Uppercase Letter 4246069
 
21.8%
Space Separator 2031999
 
10.4%
Other Punctuation 793497
 
4.1%
Other Letter 87859
 
0.5%
Decimal Number 77530
 
0.4%
Dash Punctuation 23621
 
0.1%
Currency Symbol 19104
 
0.1%
Nonspacing Mark 3106
 
< 0.1%
Open Punctuation 2309
 
< 0.1%
Other values (5) 3999
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ו 4255
 
4.8%
ש 4125
 
4.7%
م 2976
 
3.4%
א 2752
 
3.1%
י 2610
 
3.0%
س 2471
 
2.8%
ל 2159
 
2.5%
2146
 
2.4%
2090
 
2.4%
2090
 
2.4%
Other values (226) 60185
68.5%
Lowercase Letter
ValueCountFrequency (%)
a 1663962
13.6%
e 1299824
10.7%
i 1115774
 
9.1%
o 988285
 
8.1%
n 978885
 
8.0%
r 900972
 
7.4%
l 703601
 
5.8%
s 533464
 
4.4%
t 483206
 
4.0%
u 460227
 
3.8%
Other values (123) 3070508
25.2%
Uppercase Letter
ValueCountFrequency (%)
M 340160
 
8.0%
A 306511
 
7.2%
B 294046
 
6.9%
S 285592
 
6.7%
T 263762
 
6.2%
L 248184
 
5.8%
R 221130
 
5.2%
C 210035
 
4.9%
E 200770
 
4.7%
D 198524
 
4.7%
Other values (78) 1677355
39.5%
Other Punctuation
ValueCountFrequency (%)
, 740567
93.3%
. 20188
 
2.5%
& 18681
 
2.4%
' 4795
 
0.6%
! 3244
 
0.4%
: 2266
 
0.3%
* 1297
 
0.2%
" 1026
 
0.1%
/ 899
 
0.1%
@ 529
 
0.1%
Other values (2) 5
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 17224
22.2%
1 14386
18.6%
5 7540
9.7%
0 7448
9.6%
4 7253
9.4%
7 6317
 
8.1%
6 5250
 
6.8%
3 5146
 
6.6%
9 3687
 
4.8%
8 3279
 
4.2%
Nonspacing Mark
ValueCountFrequency (%)
ُ 822
26.5%
ِ 822
26.5%
346
11.1%
345
11.1%
285
 
9.2%
213
 
6.9%
112
 
3.6%
112
 
3.6%
31
 
1.0%
́ 18
 
0.6%
Currency Symbol
ValueCountFrequency (%)
$ 15468
81.0%
¥ 3635
 
19.0%
£ 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 2303
99.7%
[ 6
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 2303
99.7%
] 6
 
0.3%
Math Symbol
ValueCountFrequency (%)
+ 311
98.1%
6
 
1.9%
Space Separator
ValueCountFrequency (%)
2031999
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23621
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1302
100.0%
Other Symbol
ValueCountFrequency (%)
® 42
100.0%
Final Punctuation
ValueCountFrequency (%)
29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16385377
84.1%
Common 2952059
 
15.1%
Cyrillic 58480
 
0.3%
Han 38697
 
0.2%
Hebrew 26723
 
0.1%
Arabic 15701
 
0.1%
Thai 5224
 
< 0.1%
Katakana 2832
 
< 0.1%
Inherited 1662
 
< 0.1%
Cherokee 920
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1663962
 
10.2%
e 1299824
 
7.9%
i 1115774
 
6.8%
o 988285
 
6.0%
n 978885
 
6.0%
r 900972
 
5.5%
l 703601
 
4.3%
s 533464
 
3.3%
t 483206
 
2.9%
u 460227
 
2.8%
Other values (145) 7257177
44.3%
Han
ValueCountFrequency (%)
2146
 
5.5%
2090
 
5.4%
2090
 
5.4%
2071
 
5.4%
1819
 
4.7%
1819
 
4.7%
1755
 
4.5%
1699
 
4.4%
1699
 
4.4%
1164
 
3.0%
Other values (138) 20345
52.6%
Cyrillic
ValueCountFrequency (%)
а 4048
 
6.9%
о 3265
 
5.6%
и 3194
 
5.5%
л 2842
 
4.9%
Т 2574
 
4.4%
К 2148
 
3.7%
р 2132
 
3.6%
к 2105
 
3.6%
н 2085
 
3.6%
Р 1995
 
3.4%
Other values (51) 32092
54.9%
Common
ValueCountFrequency (%)
2031999
68.8%
, 740567
 
25.1%
- 23621
 
0.8%
. 20188
 
0.7%
& 18681
 
0.6%
2 17224
 
0.6%
$ 15468
 
0.5%
1 14386
 
0.5%
5 7540
 
0.3%
0 7448
 
0.3%
Other values (26) 54937
 
1.9%
Thai
ValueCountFrequency (%)
517
 
9.9%
440
 
8.4%
367
 
7.0%
366
 
7.0%
346
 
6.6%
345
 
6.6%
285
 
5.5%
285
 
5.5%
285
 
5.5%
253
 
4.8%
Other values (20) 1735
33.2%
Hebrew
ValueCountFrequency (%)
ו 4255
15.9%
ש 4125
15.4%
א 2752
10.3%
י 2610
9.8%
ל 2159
8.1%
ר 1724
6.5%
ן 1596
 
6.0%
ב 1528
 
5.7%
ם 1202
 
4.5%
פ 1201
 
4.5%
Other values (16) 3571
13.4%
Arabic
ValueCountFrequency (%)
م 2976
19.0%
س 2471
15.7%
ا 1763
11.2%
ر 1308
8.3%
ل 1293
8.2%
و 864
 
5.5%
ك 703
 
4.5%
د 661
 
4.2%
ي 652
 
4.2%
ب 518
 
3.3%
Other values (15) 2492
15.9%
Hangul
ValueCountFrequency (%)
24
19.0%
24
19.0%
24
19.0%
24
19.0%
24
19.0%
2
 
1.6%
2
 
1.6%
2
 
1.6%
Katakana
ValueCountFrequency (%)
708
25.0%
708
25.0%
581
20.5%
581
20.5%
127
 
4.5%
127
 
4.5%
Cherokee
ValueCountFrequency (%)
184
20.0%
184
20.0%
184
20.0%
184
20.0%
184
20.0%
Inherited
ValueCountFrequency (%)
ُ 822
49.5%
ِ 822
49.5%
́ 18
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19237066
98.7%
None 97192
 
0.5%
Cyrillic 58480
 
0.3%
CJK 38697
 
0.2%
Hebrew 26723
 
0.1%
Arabic 17345
 
0.1%
Thai 5224
 
< 0.1%
Latin Ext Additional 3143
 
< 0.1%
Katakana 2832
 
< 0.1%
Cherokee 920
 
< 0.1%
Other values (4) 179
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2031999
 
10.6%
a 1663962
 
8.6%
e 1299824
 
6.8%
i 1115774
 
5.8%
o 988285
 
5.1%
n 978885
 
5.1%
r 900972
 
4.7%
, 740567
 
3.8%
l 703601
 
3.7%
s 533464
 
2.8%
Other values (73) 8279733
43.0%
None
ValueCountFrequency (%)
é 11837
 
12.2%
á 8371
 
8.6%
ö 8177
 
8.4%
ó 7653
 
7.9%
í 7362
 
7.6%
ñ 6970
 
7.2%
¥ 3635
 
3.7%
Á 3622
 
3.7%
ë 3297
 
3.4%
ü 2927
 
3.0%
Other values (77) 33341
34.3%
Hebrew
ValueCountFrequency (%)
ו 4255
15.9%
ש 4125
15.4%
א 2752
10.3%
י 2610
9.8%
ל 2159
8.1%
ר 1724
6.5%
ן 1596
 
6.0%
ב 1528
 
5.7%
ם 1202
 
4.5%
פ 1201
 
4.5%
Other values (16) 3571
13.4%
Cyrillic
ValueCountFrequency (%)
а 4048
 
6.9%
о 3265
 
5.6%
и 3194
 
5.5%
л 2842
 
4.9%
Т 2574
 
4.4%
К 2148
 
3.7%
р 2132
 
3.6%
к 2105
 
3.6%
н 2085
 
3.6%
Р 1995
 
3.4%
Other values (51) 32092
54.9%
Arabic
ValueCountFrequency (%)
م 2976
17.2%
س 2471
14.2%
ا 1763
10.2%
ر 1308
 
7.5%
ل 1293
 
7.5%
و 864
 
5.0%
ُ 822
 
4.7%
ِ 822
 
4.7%
ك 703
 
4.1%
د 661
 
3.8%
Other values (17) 3662
21.1%
CJK
ValueCountFrequency (%)
2146
 
5.5%
2090
 
5.4%
2090
 
5.4%
2071
 
5.4%
1819
 
4.7%
1819
 
4.7%
1755
 
4.5%
1699
 
4.4%
1699
 
4.4%
1164
 
3.0%
Other values (138) 20345
52.6%
Katakana
ValueCountFrequency (%)
708
25.0%
708
25.0%
581
20.5%
581
20.5%
127
 
4.5%
127
 
4.5%
Thai
ValueCountFrequency (%)
517
 
9.9%
440
 
8.4%
367
 
7.0%
366
 
7.0%
346
 
6.6%
345
 
6.6%
285
 
5.5%
285
 
5.5%
285
 
5.5%
253
 
4.8%
Other values (20) 1735
33.2%
Latin Ext Additional
ValueCountFrequency (%)
512
16.3%
437
13.9%
329
10.5%
326
10.4%
267
8.5%
251
8.0%
196
 
6.2%
196
 
6.2%
172
 
5.5%
155
 
4.9%
Other values (9) 302
9.6%
Cherokee
ValueCountFrequency (%)
184
20.0%
184
20.0%
184
20.0%
184
20.0%
184
20.0%
Punctuation
ValueCountFrequency (%)
29
100.0%
Hangul
ValueCountFrequency (%)
24
19.0%
24
19.0%
24
19.0%
24
19.0%
24
19.0%
2
 
1.6%
2
 
1.6%
2
 
1.6%
Diacriticals
ValueCountFrequency (%)
́ 18
100.0%
Math Operators
ValueCountFrequency (%)
6
100.0%

daily_rank
Real number (ℝ)

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.490639
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2024-09-24T12:44:12.049203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median25
Q338
95-th percentile48
Maximum50
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.428645
Coefficient of variation (CV)0.566037
Kurtosis-1.2005479
Mean25.490639
Median Absolute Deviation (MAD)12
Skewness0.00063769167
Sum31180226
Variance208.18579
MonotonicityNot monotonic
2024-09-24T12:44:12.292662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 24501
 
2.0%
25 24498
 
2.0%
32 24486
 
2.0%
29 24485
 
2.0%
14 24485
 
2.0%
28 24485
 
2.0%
17 24483
 
2.0%
24 24482
 
2.0%
10 24482
 
2.0%
21 24481
 
2.0%
Other values (40) 978335
80.0%
ValueCountFrequency (%)
1 24501
2.0%
2 24473
2.0%
3 24479
2.0%
4 24478
2.0%
5 24476
2.0%
6 24481
2.0%
7 24474
2.0%
8 24476
2.0%
9 24479
2.0%
10 24482
2.0%
ValueCountFrequency (%)
50 24442
2.0%
49 24425
2.0%
48 24406
2.0%
47 24404
2.0%
46 24419
2.0%
45 24423
2.0%
44 24437
2.0%
43 24418
2.0%
42 24452
2.0%
41 24471
2.0%

daily_movement
Real number (ℝ)

ZEROS 

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77075024
Minimum-49
Maximum49
Zeros317147
Zeros (%)25.9%
Negative439415
Negative (%)35.9%
Memory size9.3 MiB
2024-09-24T12:44:12.573560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-49
5-th percentile-6
Q1-1
median0
Q32
95-th percentile9
Maximum49
Range98
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.6152135
Coefficient of variation (CV)8.5828238
Kurtosis18.042696
Mean0.77075024
Median Absolute Deviation (MAD)2
Skewness3.0390031
Sum942784
Variance43.761049
MonotonicityNot monotonic
2024-09-24T12:44:12.837890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 317147
25.9%
-1 147561
12.1%
1 144269
11.8%
-2 90842
 
7.4%
2 88315
 
7.2%
-3 58516
 
4.8%
3 57628
 
4.7%
-4 38917
 
3.2%
4 38889
 
3.2%
5 26936
 
2.2%
Other values (89) 214183
17.5%
ValueCountFrequency (%)
-49 4
< 0.1%
-48 2
 
< 0.1%
-47 1
 
< 0.1%
-46 1
 
< 0.1%
-45 2
 
< 0.1%
-44 4
< 0.1%
-43 9
< 0.1%
-42 9
< 0.1%
-41 7
< 0.1%
-40 8
< 0.1%
ValueCountFrequency (%)
49 978
0.1%
48 791
0.1%
47 797
0.1%
46 758
0.1%
45 752
0.1%
44 779
0.1%
43 768
0.1%
42 788
0.1%
41 805
0.1%
40 775
0.1%

weekly_movement
Real number (ℝ)

ZEROS 

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.466508
Minimum-49
Maximum49
Zeros138892
Zeros (%)11.4%
Negative531407
Negative (%)43.4%
Memory size9.3 MiB
2024-09-24T12:44:13.070463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-49
5-th percentile-11
Q1-3
median0
Q35
95-th percentile30
Maximum49
Range98
Interquartile range (IQR)8

Descriptive statistics

Standard deviation11.752223
Coefficient of variation (CV)4.7647213
Kurtosis3.8681089
Mean2.466508
Median Absolute Deviation (MAD)4
Skewness1.6108103
Sum3017040
Variance138.11475
MonotonicityNot monotonic
2024-09-24T12:44:13.322227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 138892
 
11.4%
-1 100179
 
8.2%
1 89508
 
7.3%
-2 80381
 
6.6%
2 65625
 
5.4%
-3 64971
 
5.3%
-4 52547
 
4.3%
3 50487
 
4.1%
-5 42589
 
3.5%
4 39853
 
3.3%
Other values (89) 498171
40.7%
ValueCountFrequency (%)
-49 1
 
< 0.1%
-48 6
 
< 0.1%
-47 4
 
< 0.1%
-46 12
 
< 0.1%
-45 15
 
< 0.1%
-44 20
 
< 0.1%
-43 23
 
< 0.1%
-42 35
< 0.1%
-41 36
< 0.1%
-40 64
< 0.1%
ValueCountFrequency (%)
49 3732
0.3%
48 3093
0.3%
47 2922
0.2%
46 2894
0.2%
45 2782
0.2%
44 2912
0.2%
43 2943
0.2%
42 2980
0.2%
41 2963
0.2%
40 2930
0.2%

country
Text

MISSING 

Distinct72
Distinct (%)< 0.1%
Missing16607
Missing (%)1.4%
Memory size68.4 MiB
2024-09-24T12:44:13.636520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2413192
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZA
2nd rowZA
3rd rowZA
4th rowZA
5th rowZA
ValueCountFrequency (%)
do 16875
 
1.4%
it 16874
 
1.4%
ni 16870
 
1.4%
pl 16864
 
1.4%
ua 16864
 
1.4%
hu 16863
 
1.4%
hn 16862
 
1.4%
sv 16862
 
1.4%
cz 16861
 
1.4%
th 16861
 
1.4%
Other values (62) 1037940
86.0%
2024-09-24T12:44:14.177498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 200514
 
8.3%
A 167151
 
6.9%
R 134644
 
5.6%
P 134555
 
5.6%
T 134511
 
5.6%
I 134184
 
5.6%
N 133947
 
5.6%
S 133823
 
5.5%
G 117343
 
4.9%
C 117033
 
4.8%
Other values (15) 1005487
41.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2413192
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 200514
 
8.3%
A 167151
 
6.9%
R 134644
 
5.6%
P 134555
 
5.6%
T 134511
 
5.6%
I 134184
 
5.6%
N 133947
 
5.6%
S 133823
 
5.5%
G 117343
 
4.9%
C 117033
 
4.8%
Other values (15) 1005487
41.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 2413192
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 200514
 
8.3%
A 167151
 
6.9%
R 134644
 
5.6%
P 134555
 
5.6%
T 134511
 
5.6%
I 134184
 
5.6%
N 133947
 
5.6%
S 133823
 
5.5%
G 117343
 
4.9%
C 117033
 
4.8%
Other values (15) 1005487
41.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2413192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 200514
 
8.3%
A 167151
 
6.9%
R 134644
 
5.6%
P 134555
 
5.6%
T 134511
 
5.6%
I 134184
 
5.6%
N 133947
 
5.6%
S 133823
 
5.5%
G 117343
 
4.9%
C 117033
 
4.8%
Other values (15) 1005487
41.7%
Distinct337
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.3 MiB
Minimum2023-10-18 00:00:00
Maximum2024-09-20 00:00:00
2024-09-24T12:44:14.471661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:44:14.732459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

popularity
Real number (ℝ)

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.690167
Minimum0
Maximum100
Zeros7161
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2024-09-24T12:44:15.009664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q166
median80
Q389
95-th percentile96
Maximum100
Range100
Interquartile range (IQR)23

Descriptive statistics

Standard deviation15.92912
Coefficient of variation (CV)0.20770746
Kurtosis2.6235648
Mean76.690167
Median Absolute Deviation (MAD)10
Skewness-1.2079235
Sum93807642
Variance253.73687
MonotonicityNot monotonic
2024-09-24T12:44:15.252750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87 44614
 
3.6%
88 41816
 
3.4%
89 39833
 
3.3%
91 39755
 
3.3%
86 39527
 
3.2%
90 39067
 
3.2%
85 36902
 
3.0%
84 35894
 
2.9%
83 35329
 
2.9%
92 32622
 
2.7%
Other values (91) 837844
68.5%
ValueCountFrequency (%)
0 7161
0.6%
1 150
 
< 0.1%
2 65
 
< 0.1%
3 248
 
< 0.1%
4 64
 
< 0.1%
5 94
 
< 0.1%
6 92
 
< 0.1%
7 113
 
< 0.1%
8 103
 
< 0.1%
9 87
 
< 0.1%
ValueCountFrequency (%)
100 10579
 
0.9%
99 13836
 
1.1%
98 15765
 
1.3%
97 16278
1.3%
96 21600
1.8%
95 24085
2.0%
94 31215
2.6%
93 32188
2.6%
92 32622
2.7%
91 39755
3.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
False
812579 
True
410624 
ValueCountFrequency (%)
False 812579
66.4%
True 410624
33.6%
2024-09-24T12:44:15.444643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

duration_ms
Real number (ℝ)

Distinct12106
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192372.56
Minimum0
Maximum939666
Zeros27
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2024-09-24T12:44:15.661268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile128841
Q1160500
median184841
Q3216386
95-th percentile274192
Maximum939666
Range939666
Interquartile range (IQR)55886

Descriptive statistics

Standard deviation50031.788
Coefficient of variation (CV)0.26007758
Kurtosis13.006145
Mean192372.56
Median Absolute Deviation (MAD)27400
Skewness2.0159689
Sum2.3531069 × 1011
Variance2.5031798 × 109
MonotonicityNot monotonic
2024-09-24T12:44:15.925756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180304 8475
 
0.7%
131872 8461
 
0.7%
210688 7622
 
0.6%
175459 7510
 
0.6%
222000 7300
 
0.6%
178426 6452
 
0.5%
210373 6116
 
0.5%
142514 6054
 
0.5%
251424 5789
 
0.5%
172797 5654
 
0.5%
Other values (12096) 1153770
94.3%
ValueCountFrequency (%)
0 27
< 0.1%
16320 1
 
< 0.1%
32187 1
 
< 0.1%
33986 2
 
< 0.1%
34285 1
 
< 0.1%
34302 1
 
< 0.1%
36026 3
 
< 0.1%
36173 4
 
< 0.1%
36826 1
 
< 0.1%
37314 4
 
< 0.1%
ValueCountFrequency (%)
939666 2
 
< 0.1%
933407 1
 
< 0.1%
931597 95
< 0.1%
821631 1
 
< 0.1%
810000 1
 
< 0.1%
808677 7
 
< 0.1%
759080 1
 
< 0.1%
757611 1
 
< 0.1%
744959 15
 
< 0.1%
743333 5
 
< 0.1%
Distinct10296
Distinct (%)0.8%
Missing626
Missing (%)0.1%
Memory size97.3 MiB
2024-09-24T12:44:16.260658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length286
Median length86
Mean length15.471119
Min length1

Characters and Unicode

Total characters18914634
Distinct characters1108
Distinct categories18 ?
Distinct scripts11 ?
Distinct blocks17 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1722 ?
Unique (%)0.1%

Sample

1st rowDie With A Smile
2nd rowHIT ME HARD AND SOFT
3rd rowShort n' Sweet
4th rowMUSE
5th rowEspresso
ValueCountFrequency (%)
the 86119
 
2.5%
54273
 
1.6%
a 37251
 
1.1%
la 34718
 
1.0%
mañana 34359
 
1.0%
me 33541
 
1.0%
el 29296
 
0.9%
feat 26058
 
0.8%
que 25784
 
0.8%
and 25518
 
0.7%
Other values (13585) 3044169
88.7%
2024-09-24T12:44:16.918280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2208509
 
11.7%
e 1193659
 
6.3%
a 1156534
 
6.1%
i 784246
 
4.1%
o 720537
 
3.8%
n 682168
 
3.6%
r 656999
 
3.5%
t 540508
 
2.9%
s 536856
 
2.8%
l 487250
 
2.6%
Other values (1098) 9947368
52.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9742111
51.5%
Uppercase Letter 5571064
29.5%
Space Separator 2208509
 
11.7%
Other Letter 517914
 
2.7%
Other Punctuation 286851
 
1.5%
Decimal Number 217536
 
1.2%
Close Punctuation 156363
 
0.8%
Open Punctuation 156123
 
0.8%
Dash Punctuation 23201
 
0.1%
Nonspacing Mark 17286
 
0.1%
Other values (8) 17676
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 34041
 
6.6%
ي 28709
 
5.5%
ل 19129
 
3.7%
ن 15609
 
3.0%
م 15247
 
2.9%
י 13292
 
2.6%
ו 12694
 
2.5%
و 12484
 
2.4%
ب 11628
 
2.2%
ت 10402
 
2.0%
Other values (704) 344679
66.6%
Lowercase Letter
ValueCountFrequency (%)
e 1193659
12.3%
a 1156534
11.9%
i 784246
 
8.1%
o 720537
 
7.4%
n 682168
 
7.0%
r 656999
 
6.7%
t 540508
 
5.5%
s 536856
 
5.5%
l 487250
 
5.0%
u 330847
 
3.4%
Other values (170) 2652507
27.2%
Uppercase Letter
ValueCountFrequency (%)
A 470815
 
8.5%
E 465119
 
8.3%
S 429647
 
7.7%
T 427993
 
7.7%
O 381079
 
6.8%
I 320877
 
5.8%
L 286906
 
5.1%
M 274160
 
4.9%
R 273890
 
4.9%
N 256066
 
4.6%
Other values (112) 1984512
35.6%
Other Punctuation
ValueCountFrequency (%)
. 84083
29.3%
' 57916
20.2%
, 33802
11.8%
: 26080
 
9.1%
& 26059
 
9.1%
" 25256
 
8.8%
? 13517
 
4.7%
/ 6167
 
2.1%
! 5894
 
2.1%
# 4400
 
1.5%
Other values (13) 3677
 
1.3%
Nonspacing Mark
ValueCountFrequency (%)
3848
22.3%
3325
19.2%
2943
17.0%
2748
15.9%
1440
 
8.3%
700
 
4.0%
546
 
3.2%
353
 
2.0%
342
 
2.0%
263
 
1.5%
Other values (5) 778
 
4.5%
Decimal Number
ValueCountFrequency (%)
1 56654
26.0%
2 40629
18.7%
0 40173
18.5%
3 18173
 
8.4%
4 17145
 
7.9%
9 15177
 
7.0%
8 11297
 
5.2%
5 7808
 
3.6%
7 7053
 
3.2%
6 3192
 
1.5%
Other values (3) 235
 
0.1%
Other Symbol
ValueCountFrequency (%)
56
17.8%
🇪 50
15.9%
🇩 50
15.9%
® 42
13.4%
40
12.7%
🍸 19
 
6.1%
💀 17
 
5.4%
💔 16
 
5.1%
🦚 16
 
5.1%
🐕 8
 
2.5%
Math Symbol
ValueCountFrequency (%)
| 4911
70.9%
+ 975
 
14.1%
< 495
 
7.1%
332
 
4.8%
> 118
 
1.7%
÷ 44
 
0.6%
= 37
 
0.5%
× 12
 
0.2%
1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 149347
95.7%
[ 6294
 
4.0%
388
 
0.2%
78
 
< 0.1%
16
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 149603
95.7%
] 6294
 
4.0%
388
 
0.2%
78
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 22954
98.9%
247
 
1.1%
Currency Symbol
ValueCountFrequency (%)
$ 3260
99.8%
8
 
0.2%
Final Punctuation
ValueCountFrequency (%)
2916
90.4%
310
 
9.6%
Modifier Letter
ValueCountFrequency (%)
2674
99.1%
23
 
0.9%
Initial Punctuation
ValueCountFrequency (%)
328
71.8%
129
 
28.2%
Space Separator
ValueCountFrequency (%)
2208509
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 773
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14994996
79.3%
Common 3066001
 
16.2%
Cyrillic 317961
 
1.7%
Arabic 232693
 
1.2%
Hebrew 106932
 
0.6%
Thai 87822
 
0.5%
Han 82502
 
0.4%
Katakana 16425
 
0.1%
Hiragana 8455
 
< 0.1%
Inherited 601
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
2368
 
2.9%
1651
 
2.0%
1270
 
1.5%
1178
 
1.4%
1164
 
1.4%
1139
 
1.4%
1105
 
1.3%
1080
 
1.3%
981
 
1.2%
960
 
1.2%
Other values (518) 69606
84.4%
Latin
ValueCountFrequency (%)
e 1193659
 
8.0%
a 1156534
 
7.7%
i 784246
 
5.2%
o 720537
 
4.8%
n 682168
 
4.5%
r 656999
 
4.4%
t 540508
 
3.6%
s 536856
 
3.6%
l 487250
 
3.2%
A 470815
 
3.1%
Other values (206) 7765424
51.8%
Common
ValueCountFrequency (%)
2208509
72.0%
) 149603
 
4.9%
( 149347
 
4.9%
. 84083
 
2.7%
' 57916
 
1.9%
1 56654
 
1.8%
2 40629
 
1.3%
0 40173
 
1.3%
, 33802
 
1.1%
: 26080
 
0.9%
Other values (63) 219205
 
7.1%
Cyrillic
ValueCountFrequency (%)
а 27621
 
8.7%
о 25031
 
7.9%
е 24596
 
7.7%
и 21552
 
6.8%
т 17972
 
5.7%
н 16165
 
5.1%
р 12604
 
4.0%
л 12530
 
3.9%
м 11938
 
3.8%
к 10447
 
3.3%
Other values (62) 137505
43.2%
Thai
ValueCountFrequency (%)
6183
 
7.0%
5028
 
5.7%
4693
 
5.3%
4283
 
4.9%
3938
 
4.5%
3848
 
4.4%
3774
 
4.3%
3408
 
3.9%
3366
 
3.8%
3325
 
3.8%
Other values (44) 45976
52.4%
Katakana
ValueCountFrequency (%)
1480
 
9.0%
1254
 
7.6%
1044
 
6.4%
851
 
5.2%
829
 
5.0%
681
 
4.1%
650
 
4.0%
632
 
3.8%
608
 
3.7%
604
 
3.7%
Other values (37) 7792
47.4%
Arabic
ValueCountFrequency (%)
ا 34041
14.6%
ي 28709
12.3%
ل 19129
 
8.2%
ن 15609
 
6.7%
م 15247
 
6.6%
و 12484
 
5.4%
ب 11628
 
5.0%
ت 10402
 
4.5%
ر 8938
 
3.8%
ع 8401
 
3.6%
Other values (29) 68105
29.3%
Hiragana
ValueCountFrequency (%)
997
 
11.8%
670
 
7.9%
640
 
7.6%
532
 
6.3%
472
 
5.6%
472
 
5.6%
440
 
5.2%
370
 
4.4%
337
 
4.0%
337
 
4.0%
Other values (23) 3188
37.7%
Hebrew
ValueCountFrequency (%)
י 13292
12.4%
ו 12694
 
11.9%
ל 8145
 
7.6%
א 7072
 
6.6%
ר 6859
 
6.4%
ה 6174
 
5.8%
ת 5186
 
4.8%
ש 5064
 
4.7%
מ 4964
 
4.6%
ם 4558
 
4.3%
Other values (17) 32924
30.8%
Greek
ValueCountFrequency (%)
Λ 51
20.7%
α 50
20.3%
ρ 25
10.2%
ί 20
 
8.1%
τ 15
 
6.1%
γ 10
 
4.1%
μ 10
 
4.1%
σ 10
 
4.1%
Τ 10
 
4.1%
ν 10
 
4.1%
Other values (5) 35
14.2%
Inherited
ValueCountFrequency (%)
ً 248
41.3%
ّ 146
24.3%
ِ 146
24.3%
61
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17808790
94.2%
Cyrillic 317961
 
1.7%
Arabic 233486
 
1.2%
None 227912
 
1.2%
Hebrew 106932
 
0.6%
Thai 87822
 
0.5%
CJK 82479
 
0.4%
Katakana 19099
 
0.1%
Latin Ext Additional 17036
 
0.1%
Hiragana 8455
 
< 0.1%
Other values (7) 4662
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2208509
 
12.4%
e 1193659
 
6.7%
a 1156534
 
6.5%
i 784246
 
4.4%
o 720537
 
4.0%
n 682168
 
3.8%
r 656999
 
3.7%
t 540508
 
3.0%
s 536856
 
3.0%
l 487250
 
2.7%
Other values (79) 8841524
49.6%
Arabic
ValueCountFrequency (%)
ا 34041
14.6%
ي 28709
12.3%
ل 19129
 
8.2%
ن 15609
 
6.7%
م 15247
 
6.5%
و 12484
 
5.3%
ب 11628
 
5.0%
ت 10402
 
4.5%
ر 8938
 
3.8%
ع 8401
 
3.6%
Other values (34) 68898
29.5%
Cyrillic
ValueCountFrequency (%)
а 27621
 
8.7%
о 25031
 
7.9%
е 24596
 
7.7%
и 21552
 
6.8%
т 17972
 
5.7%
н 16165
 
5.1%
р 12604
 
4.0%
л 12530
 
3.9%
м 11938
 
3.8%
к 10447
 
3.3%
Other values (62) 137505
43.2%
None
ValueCountFrequency (%)
Á 26696
 
11.7%
Ñ 25081
 
11.0%
ñ 17511
 
7.7%
á 14686
 
6.4%
í 12119
 
5.3%
ó 11267
 
4.9%
é 9616
 
4.2%
É 8849
 
3.9%
ä 7948
 
3.5%
ö 6933
 
3.0%
Other values (146) 87206
38.3%
Hebrew
ValueCountFrequency (%)
י 13292
12.4%
ו 12694
 
11.9%
ל 8145
 
7.6%
א 7072
 
6.6%
ר 6859
 
6.4%
ה 6174
 
5.8%
ת 5186
 
4.8%
ש 5064
 
4.7%
מ 4964
 
4.6%
ם 4558
 
4.3%
Other values (17) 32924
30.8%
Thai
ValueCountFrequency (%)
6183
 
7.0%
5028
 
5.7%
4693
 
5.3%
4283
 
4.9%
3938
 
4.5%
3848
 
4.4%
3774
 
4.3%
3408
 
3.9%
3366
 
3.8%
3325
 
3.8%
Other values (44) 45976
52.4%
Punctuation
ValueCountFrequency (%)
2916
71.8%
328
 
8.1%
310
 
7.6%
247
 
6.1%
129
 
3.2%
113
 
2.8%
16
 
0.4%
5
 
0.1%
Katakana
ValueCountFrequency (%)
2674
 
14.0%
1480
 
7.7%
1254
 
6.6%
1044
 
5.5%
851
 
4.5%
829
 
4.3%
681
 
3.6%
650
 
3.4%
632
 
3.3%
608
 
3.2%
Other values (38) 8396
44.0%
CJK
ValueCountFrequency (%)
2368
 
2.9%
1651
 
2.0%
1270
 
1.5%
1178
 
1.4%
1164
 
1.4%
1139
 
1.4%
1105
 
1.3%
1080
 
1.3%
981
 
1.2%
960
 
1.2%
Other values (517) 69583
84.4%
Latin Ext Additional
ValueCountFrequency (%)
1493
 
8.8%
1316
 
7.7%
1287
 
7.6%
1126
 
6.6%
1103
 
6.5%
958
 
5.6%
883
 
5.2%
831
 
4.9%
ế 766
 
4.5%
729
 
4.3%
Other values (32) 6544
38.4%
Hiragana
ValueCountFrequency (%)
997
 
11.8%
670
 
7.9%
640
 
7.6%
532
 
6.3%
472
 
5.6%
472
 
5.6%
440
 
5.2%
370
 
4.4%
337
 
4.0%
337
 
4.0%
Other values (23) 3188
37.7%
Misc Technical
ValueCountFrequency (%)
332
100.0%
VS
ValueCountFrequency (%)
61
100.0%
Misc Symbols
ValueCountFrequency (%)
56
58.3%
40
41.7%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🇪 50
50.0%
🇩 50
50.0%
Currency Symbols
ValueCountFrequency (%)
8
100.0%
Math Operators
ValueCountFrequency (%)
1
100.0%
Distinct2184
Distinct (%)0.2%
Missing464
Missing (%)< 0.1%
Memory size9.3 MiB
Minimum1900-01-01 00:00:00
Maximum2024-09-18 00:00:00
2024-09-24T12:44:17.165123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:44:17.441044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

danceability
Real number (ℝ)

Distinct767
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.68601983
Minimum0
Maximum0.988
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2024-09-24T12:44:17.718143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.445
Q10.596
median0.704
Q30.788
95-th percentile0.89
Maximum0.988
Range0.988
Interquartile range (IQR)0.192

Descriptive statistics

Standard deviation0.13708993
Coefficient of variation (CV)0.19983378
Kurtosis-0.13163523
Mean0.68601983
Median Absolute Deviation (MAD)0.092
Skewness-0.50937749
Sum839141.51
Variance0.01879365
MonotonicityNot monotonic
2024-09-24T12:44:17.993221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.841 11260
 
0.9%
0.701 9799
 
0.8%
0.561 9604
 
0.8%
0.741 9573
 
0.8%
0.75 9402
 
0.8%
0.774 9176
 
0.8%
0.747 8910
 
0.7%
0.472 8783
 
0.7%
0.552 8705
 
0.7%
0.638 8383
 
0.7%
Other values (757) 1129608
92.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.0657 1
 
< 0.1%
0.0668 3
 
< 0.1%
0.0811 1
 
< 0.1%
0.0997 10
< 0.1%
0.124 11
< 0.1%
0.131 1
 
< 0.1%
0.147 6
< 0.1%
0.154 1
 
< 0.1%
0.158 2
 
< 0.1%
ValueCountFrequency (%)
0.988 65
< 0.1%
0.982 1
 
< 0.1%
0.98 72
< 0.1%
0.979 2
 
< 0.1%
0.978 41
 
< 0.1%
0.977 16
 
< 0.1%
0.976 8
 
< 0.1%
0.975 10
 
< 0.1%
0.974 131
< 0.1%
0.971 101
< 0.1%

energy
Real number (ℝ)

HIGH CORRELATION 

Distinct936
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65270202
Minimum2.01 × 10-5
Maximum0.998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2024-09-24T12:44:18.262146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.01 × 10-5
5-th percentile0.371
Q10.552
median0.672
Q30.764
95-th percentile0.893
Maximum0.998
Range0.9979799
Interquartile range (IQR)0.212

Descriptive statistics

Standard deviation0.16226598
Coefficient of variation (CV)0.24860653
Kurtosis0.32885053
Mean0.65270202
Median Absolute Deviation (MAD)0.107
Skewness-0.56027481
Sum798387.07
Variance0.026330249
MonotonicityNot monotonic
2024-09-24T12:44:18.535348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.738 11771
 
1.0%
0.733 11721
 
1.0%
0.668 9953
 
0.8%
0.471 9267
 
0.8%
0.831 8323
 
0.7%
0.702 8305
 
0.7%
0.604 8240
 
0.7%
0.76 7966
 
0.7%
0.679 7808
 
0.6%
0.62 7068
 
0.6%
Other values (926) 1132781
92.6%
ValueCountFrequency (%)
2.01 × 10-5117
< 0.1%
0.00189 1
 
< 0.1%
0.00588 10
 
< 0.1%
0.00738 10
 
< 0.1%
0.0085 4
 
< 0.1%
0.0122 12
 
< 0.1%
0.0124 2
 
< 0.1%
0.0173 12
 
< 0.1%
0.0228 1
 
< 0.1%
0.0242 268
< 0.1%
ValueCountFrequency (%)
0.998 2
 
< 0.1%
0.997 2
 
< 0.1%
0.995 16
 
< 0.1%
0.993 2
 
< 0.1%
0.992 133
< 0.1%
0.991 115
< 0.1%
0.99 23
 
< 0.1%
0.989 175
< 0.1%
0.988 7
 
< 0.1%
0.987 172
< 0.1%

key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5250347
Minimum0
Maximum11
Zeros105243
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2024-09-24T12:44:18.794777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.5734501
Coefficient of variation (CV)0.64677425
Kurtosis-1.2886337
Mean5.5250347
Median Absolute Deviation (MAD)3
Skewness-0.055281838
Sum6758239
Variance12.769546
MonotonicityNot monotonic
2024-09-24T12:44:19.053286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 146416
12.0%
9 122288
10.0%
11 119035
9.7%
5 113044
9.2%
2 112313
9.2%
7 112258
9.2%
0 105243
8.6%
6 104583
8.5%
8 94591
7.7%
10 84211
6.9%
Other values (2) 109221
8.9%
ValueCountFrequency (%)
0 105243
8.6%
1 146416
12.0%
2 112313
9.2%
3 37026
 
3.0%
4 72195
5.9%
5 113044
9.2%
6 104583
8.5%
7 112258
9.2%
8 94591
7.7%
9 122288
10.0%
ValueCountFrequency (%)
11 119035
9.7%
10 84211
6.9%
9 122288
10.0%
8 94591
7.7%
7 112258
9.2%
6 104583
8.5%
5 113044
9.2%
4 72195
5.9%
3 37026
 
3.0%
2 112313
9.2%

loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct7693
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.4260652
Minimum-37.334
Maximum3.233
Zeros0
Zeros (%)0.0%
Negative1221707
Negative (%)99.9%
Memory size9.3 MiB
2024-09-24T12:44:19.308206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-37.334
5-th percentile-11.214
Q1-7.777
median-5.966
Q3-4.706
95-th percentile-3.049
Maximum3.233
Range40.567
Interquartile range (IQR)3.071

Descriptive statistics

Standard deviation2.5804953
Coefficient of variation (CV)-0.40156693
Kurtosis3.6621174
Mean-6.4260652
Median Absolute Deviation (MAD)1.507
Skewness-1.1673856
Sum-7860382.3
Variance6.6589559
MonotonicityNot monotonic
2024-09-24T12:44:19.567008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.692 8975
 
0.7%
-4.409 7622
 
0.6%
-3.18 7584
 
0.6%
-5.478 7019
 
0.6%
-5.707 6459
 
0.5%
-4.263 6156
 
0.5%
-10.171 6120
 
0.5%
-5.505 6111
 
0.5%
-8.472 5965
 
0.5%
-2.888 5917
 
0.5%
Other values (7683) 1155275
94.4%
ValueCountFrequency (%)
-37.334 10
< 0.1%
-34.915 10
< 0.1%
-33.655 12
< 0.1%
-32.861 4
 
< 0.1%
-32.705 4
 
< 0.1%
-32.135 3
 
< 0.1%
-31.485 3
 
< 0.1%
-31.356 7
< 0.1%
-31.338 3
 
< 0.1%
-31.328 12
< 0.1%
ValueCountFrequency (%)
3.233 179
< 0.1%
2.619 1
 
< 0.1%
2.605 10
 
< 0.1%
2.006 16
 
< 0.1%
1.808 69
 
< 0.1%
1.507 1
 
< 0.1%
1.155 169
< 0.1%
1.047 113
< 0.1%
0.856 127
< 0.1%
0.826 7
 
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.7 MiB
1
659402 
0
563801 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1223203
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 659402
53.9%
0 563801
46.1%

Length

2024-09-24T12:44:19.831167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-24T12:44:20.016517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 659402
53.9%
0 563801
46.1%

Most occurring characters

ValueCountFrequency (%)
1 659402
53.9%
0 563801
46.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1223203
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 659402
53.9%
0 563801
46.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1223203
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 659402
53.9%
0 563801
46.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1223203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 659402
53.9%
0 563801
46.1%

speechiness
Real number (ℝ)

Distinct1246
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.094986404
Minimum0
Maximum0.937
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2024-09-24T12:44:20.497124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.029
Q10.0398
median0.0584
Q30.111
95-th percentile0.301
Maximum0.937
Range0.937
Interquartile range (IQR)0.0712

Descriptive statistics

Standard deviation0.089796774
Coefficient of variation (CV)0.94536449
Kurtosis5.6867191
Mean0.094986404
Median Absolute Deviation (MAD)0.0233
Skewness2.2267765
Sum116187.65
Variance0.0080634606
MonotonicityNot monotonic
2024-09-24T12:44:20.794030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0285 12214
 
1.0%
0.0319 9784
 
0.8%
0.0603 8849
 
0.7%
0.0447 8709
 
0.7%
0.0337 8607
 
0.7%
0.0643 7676
 
0.6%
0.0358 7635
 
0.6%
0.157 7338
 
0.6%
0.0321 7120
 
0.6%
0.0381 6869
 
0.6%
Other values (1236) 1138402
93.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.0219 1
 
< 0.1%
0.0229 2
 
< 0.1%
0.023 41
 
< 0.1%
0.0232 213
< 0.1%
0.0236 3
 
< 0.1%
0.0237 332
< 0.1%
0.024 1
 
< 0.1%
0.0242 204
< 0.1%
0.0243 196
< 0.1%
ValueCountFrequency (%)
0.937 1
 
< 0.1%
0.921 8
< 0.1%
0.912 1
 
< 0.1%
0.896 1
 
< 0.1%
0.884 2
 
< 0.1%
0.811 14
< 0.1%
0.791 1
 
< 0.1%
0.784 1
 
< 0.1%
0.775 4
 
< 0.1%
0.766 1
 
< 0.1%

acousticness
Real number (ℝ)

Distinct2459
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27057465
Minimum3.45 × 10-6
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2024-09-24T12:44:21.147520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.45 × 10-6
5-th percentile0.00529
Q10.067
median0.183
Q30.434
95-th percentile0.794
Maximum0.996
Range0.99599655
Interquartile range (IQR)0.367

Descriptive statistics

Standard deviation0.24913747
Coefficient of variation (CV)0.92077167
Kurtosis-0.065310215
Mean0.27057465
Median Absolute Deviation (MAD)0.145
Skewness0.97014535
Sum330967.73
Variance0.062069481
MonotonicityNot monotonic
2024-09-24T12:44:21.533738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.183 11388
 
0.9%
0.117 10644
 
0.9%
0.151 10013
 
0.8%
0.107 9000
 
0.7%
0.446 8681
 
0.7%
0.256 8303
 
0.7%
0.199 8136
 
0.7%
0.2 8093
 
0.7%
0.255 7600
 
0.6%
0.131 6513
 
0.5%
Other values (2449) 1134832
92.8%
ValueCountFrequency (%)
3.45 × 10-61
 
< 0.1%
7.53 × 10-6202
< 0.1%
9.27 × 10-69
 
< 0.1%
1.26 × 10-53
 
< 0.1%
1.37 × 10-56
 
< 0.1%
1.45 × 10-52
 
< 0.1%
1.53 × 10-52
 
< 0.1%
1.83 × 10-54
 
< 0.1%
2.04 × 10-51
 
< 0.1%
2.31 × 10-519
 
< 0.1%
ValueCountFrequency (%)
0.996 6
 
< 0.1%
0.995 2
 
< 0.1%
0.994 19
< 0.1%
0.993 4
 
< 0.1%
0.992 1
 
< 0.1%
0.991 4
 
< 0.1%
0.99 4
 
< 0.1%
0.989 4
 
< 0.1%
0.988 7
 
< 0.1%
0.987 7
 
< 0.1%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct3419
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.017951602
Minimum0
Maximum0.974
Zeros589443
Zeros (%)48.2%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2024-09-24T12:44:21.854022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.34 × 10-6
Q37.76 × 10-5
95-th percentile0.0612
Maximum0.974
Range0.974
Interquartile range (IQR)7.76 × 10-5

Descriptive statistics

Standard deviation0.094064144
Coefficient of variation (CV)5.2398747
Kurtosis55.408364
Mean0.017951602
Median Absolute Deviation (MAD)1.34 × 10-6
Skewness7.1461371
Sum21958.453
Variance0.0088480632
MonotonicityNot monotonic
2024-09-24T12:44:22.123756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 589443
48.2%
1.9 × 10-57763
 
0.6%
6.54 × 10-57589
 
0.6%
0.0106 6805
 
0.6%
2.06 × 10-56697
 
0.5%
0.0608 6117
 
0.5%
2.41 × 10-55912
 
0.5%
0.000809 5790
 
0.5%
2.16 × 10-55763
 
0.5%
1.11 × 10-65176
 
0.4%
Other values (3409) 576148
47.1%
ValueCountFrequency (%)
0 589443
48.2%
1 × 10-6406
 
< 0.1%
1.01 × 10-6294
 
< 0.1%
1.02 × 10-6771
 
0.1%
1.03 × 10-699
 
< 0.1%
1.04 × 10-6160
 
< 0.1%
1.05 × 10-61888
 
0.2%
1.06 × 10-61102
 
0.1%
1.07 × 10-6133
 
< 0.1%
1.08 × 10-6187
 
< 0.1%
ValueCountFrequency (%)
0.974 1
 
< 0.1%
0.971 5
 
< 0.1%
0.97 14
 
< 0.1%
0.968 63
< 0.1%
0.967 4
 
< 0.1%
0.965 1
 
< 0.1%
0.963 14
 
< 0.1%
0.961 12
 
< 0.1%
0.959 113
< 0.1%
0.955 1
 
< 0.1%

liveness
Real number (ℝ)

Distinct1360
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1701289
Minimum0.0139
Maximum0.978
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2024-09-24T12:44:22.372571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.0139
5-th percentile0.0642
Q10.0957
median0.119
Q30.205
95-th percentile0.403
Maximum0.978
Range0.9641
Interquartile range (IQR)0.1093

Descriptive statistics

Standard deviation0.12403824
Coefficient of variation (CV)0.72908389
Kurtosis6.6738219
Mean0.1701289
Median Absolute Deviation (MAD)0.0355
Skewness2.2725725
Sum208102.18
Variance0.015385484
MonotonicityNot monotonic
2024-09-24T12:44:22.643248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.104 22204
 
1.8%
0.114 19208
 
1.6%
0.108 18789
 
1.5%
0.119 18427
 
1.5%
0.105 17846
 
1.5%
0.11 17104
 
1.4%
0.112 16293
 
1.3%
0.117 14009
 
1.1%
0.109 13981
 
1.1%
0.113 13791
 
1.1%
Other values (1350) 1051551
86.0%
ValueCountFrequency (%)
0.0139 49
 
< 0.1%
0.0145 71
 
< 0.1%
0.0154 184
< 0.1%
0.0172 15
 
< 0.1%
0.0187 246
< 0.1%
0.0194 1
 
< 0.1%
0.0195 2
 
< 0.1%
0.0196 100
< 0.1%
0.0197 20
 
< 0.1%
0.0211 7
 
< 0.1%
ValueCountFrequency (%)
0.978 17
 
< 0.1%
0.973 2
 
< 0.1%
0.968 4
 
< 0.1%
0.967 1
 
< 0.1%
0.965 1
 
< 0.1%
0.964 100
 
< 0.1%
0.963 236
< 0.1%
0.958 277
< 0.1%
0.955 28
 
< 0.1%
0.953 91
 
< 0.1%

valence
Real number (ℝ)

Distinct1098
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54986002
Minimum0
Maximum0.992
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2024-09-24T12:44:22.907131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.178
Q10.37
median0.551
Q30.734
95-th percentile0.912
Maximum0.992
Range0.992
Interquartile range (IQR)0.364

Descriptive statistics

Standard deviation0.22796901
Coefficient of variation (CV)0.41459463
Kurtosis-0.93218638
Mean0.54986002
Median Absolute Deviation (MAD)0.181
Skewness-0.059252305
Sum672590.43
Variance0.051969871
MonotonicityNot monotonic
2024-09-24T12:44:23.221061image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.844 10589
 
0.9%
0.69 9263
 
0.8%
0.219 9113
 
0.7%
0.934 8255
 
0.7%
0.242 8035
 
0.7%
0.564 7645
 
0.6%
0.563 7335
 
0.6%
0.446 7177
 
0.6%
0.438 7163
 
0.6%
0.747 7153
 
0.6%
Other values (1088) 1141475
93.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 × 10-51
 
< 0.1%
0.0271 1
 
< 0.1%
0.0281 1
 
< 0.1%
0.0299 1
 
< 0.1%
0.0322 3
 
< 0.1%
0.0334 5
 
< 0.1%
0.0344 7
 
< 0.1%
0.0347 35
 
< 0.1%
0.0348 229
< 0.1%
ValueCountFrequency (%)
0.992 1
 
< 0.1%
0.989 5
 
< 0.1%
0.985 6
 
< 0.1%
0.983 4
 
< 0.1%
0.981 10
 
< 0.1%
0.979 3
 
< 0.1%
0.978 371
< 0.1%
0.977 17
 
< 0.1%
0.976 90
 
< 0.1%
0.975 238
< 0.1%

tempo
Real number (ℝ)

HIGH CORRELATION 

Distinct11218
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.30626
Minimum0
Maximum235.907
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2024-09-24T12:44:23.474415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile81.882
Q1100.007
median119.967
Q3140.098
95-th percentile173.85
Maximum235.907
Range235.907
Interquartile range (IQR)40.091

Descriptive statistics

Standard deviation28.073789
Coefficient of variation (CV)0.22953681
Kurtosis-0.32577056
Mean122.30626
Median Absolute Deviation (MAD)19.988
Skewness0.47160197
Sum1.4960538 × 108
Variance788.13765
MonotonicityNot monotonic
2024-09-24T12:44:23.714510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105.029 8635
 
0.7%
124.987 7892
 
0.6%
159.92 7677
 
0.6%
111.018 7580
 
0.6%
103.969 6926
 
0.6%
169.994 6452
 
0.5%
99.986 6264
 
0.5%
104.978 6116
 
0.5%
151.647 6054
 
0.5%
117.038 5789
 
0.5%
Other values (11208) 1153818
94.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
36.648 1
 
< 0.1%
46.718 18
 
< 0.1%
47.914 1
 
< 0.1%
48.718 19
 
< 0.1%
53.376 1679
0.1%
53.443 5
 
< 0.1%
55.067 1
 
< 0.1%
56.829 8
 
< 0.1%
57.18 17
 
< 0.1%
ValueCountFrequency (%)
235.907 42
 
< 0.1%
227.895 2
 
< 0.1%
219.327 7
 
< 0.1%
217.969 35
 
< 0.1%
214.047 216
< 0.1%
214.034 55
 
< 0.1%
213.503 16
 
< 0.1%
211.718 8
 
< 0.1%
210.224 22
 
< 0.1%
209.942 27
 
< 0.1%

time_signature
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.7 MiB
4
1103671 
3
 
94735
5
 
13402
1
 
11394
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1223203
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row3
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 1103671
90.2%
3 94735
 
7.7%
5 13402
 
1.1%
1 11394
 
0.9%
0 1
 
< 0.1%

Length

2024-09-24T12:44:24.004458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-24T12:44:24.276658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
4 1103671
90.2%
3 94735
 
7.7%
5 13402
 
1.1%
1 11394
 
0.9%
0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
4 1103671
90.2%
3 94735
 
7.7%
5 13402
 
1.1%
1 11394
 
0.9%
0 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1223203
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1103671
90.2%
3 94735
 
7.7%
5 13402
 
1.1%
1 11394
 
0.9%
0 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1223203
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1103671
90.2%
3 94735
 
7.7%
5 13402
 
1.1%
1 11394
 
0.9%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1223203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 1103671
90.2%
3 94735
 
7.7%
5 13402
 
1.1%
1 11394
 
0.9%
0 1
 
< 0.1%

Interactions

2024-09-24T12:43:49.078695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:17.508171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:24.626772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:30.127525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:35.999348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:42.539578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:50.604791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:01.956606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:08.277128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:14.035516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:19.962879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:26.031846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:32.043936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:37.708255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:43.350828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:49.466997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:17.916567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:24.975044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:30.520904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:36.396800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:43.098272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:51.304903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:02.371385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:08.653496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:14.404286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:20.321299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:26.393362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:32.615287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:38.074121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:43.707980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:49.824863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:18.282697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:25.323636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:30.878172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:36.773930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:43.497063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:52.435895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:02.803647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:09.046625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:14.815003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:20.714886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:26.776503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:32.973116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:38.426864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:44.060347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:50.175802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:18.708181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:25.686211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:31.258733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:37.116126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:44.004988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:52.865912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:03.362775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:09.429950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:15.188271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:21.117394image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:27.443349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:33.350589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:38.787599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:44.426225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:50.519747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:20.147534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:26.086808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:31.720749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:37.590254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:44.425091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:53.261752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:03.836609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:09.824455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:15.581351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:21.538736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:27.826990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:33.697694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:39.134965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:44.839207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:50.914856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:20.529465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:26.430065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:32.130334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:38.051014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:44.893180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:53.839861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:04.212987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:10.219192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:15.990037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:22.020418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:28.209252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:34.081306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:39.515376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:45.226547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:51.370615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:20.904362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:26.802807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:32.722118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:38.508628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:45.471759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:54.374844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:04.593870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:10.661903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:16.412877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:22.428516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:28.557239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:34.420323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:39.876164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:45.606550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:51.726103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:21.409699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:27.201412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:33.117539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:39.025405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:45.999295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:54.816540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:04.957422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:11.067367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:16.779680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:22.944605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:28.926725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:34.762955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:40.270464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:45.968964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:52.112531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:21.823204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:27.539312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:33.460212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:39.462815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:46.477536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:55.400005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:05.346007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:11.420738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:17.158319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:23.435584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:29.317583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:35.177154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:40.635757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:46.352567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:52.479369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:22.195914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:27.970867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:33.843064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-09-24T12:42:46.893774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-09-24T12:43:05.806902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:11.807051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:17.570457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:23.794556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:29.698725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-09-24T12:43:52.867890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:22.578080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:28.305214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:34.195316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:40.316277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:47.264104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:57.865249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:06.172963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:12.210123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:17.970341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:24.176519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:30.127493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:35.906373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:41.453877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:47.150615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:53.318027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:23.011659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:28.651865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:34.579640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:40.656363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:48.026387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:59.860219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:06.576830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:12.574158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:18.346510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:24.545212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:30.516738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:36.225323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:41.824790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:47.555707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:53.668844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:23.366072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:29.030102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:34.934315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:41.029020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:48.455980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:00.472862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:07.100683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:12.954445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:18.738131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:24.913295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:30.955267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:36.576963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:42.193763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:48.006126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:54.051338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:23.758526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:29.359834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:35.273630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:41.469732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:48.919815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:00.915933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:07.509215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:13.318046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:19.152334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:25.268759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:31.328302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:36.932315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:42.602601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:48.347082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:54.378906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:24.206023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:29.720893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:35.614746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:42.044310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:42:49.336689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:01.385418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:07.891738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:13.670853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:19.545739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:25.625223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:31.687602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:37.319591image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:42.995895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-24T12:43:48.720804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-09-24T12:44:24.594146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
acousticnessdaily_movementdaily_rankdanceabilityduration_msenergyinstrumentalnessis_explicitkeylivenessloudnessmodepopularityspeechinesstempotime_signaturevalenceweekly_movement
acousticness1.0000.0050.022-0.1620.046-0.429-0.0420.192-0.0040.005-0.3550.121-0.118-0.069-0.0980.144-0.0970.011
daily_movement0.0051.000-0.076-0.015-0.002-0.0030.0020.031-0.0020.000-0.0050.016-0.040-0.019-0.0020.0080.0050.277
daily_rank0.022-0.0761.000-0.0730.055-0.0350.0070.032-0.0290.020-0.0450.012-0.1330.0020.0210.026-0.059-0.176
danceability-0.162-0.015-0.0731.000-0.2130.1760.0840.293-0.003-0.1620.1860.200-0.0270.331-0.1610.2430.385-0.037
duration_ms0.046-0.0020.055-0.2131.000-0.1230.0030.060-0.053-0.034-0.1170.0760.043-0.170-0.0440.094-0.2310.004
energy-0.429-0.003-0.0350.176-0.1231.000-0.0760.1850.0300.1090.6920.1420.0030.1310.1020.1210.340-0.011
instrumentalness-0.0420.0020.0070.0840.003-0.0761.0000.0650.003-0.092-0.2160.0570.056-0.0630.0290.057-0.073-0.006
is_explicit0.1920.0310.0320.2930.0600.1850.0651.0000.1160.1200.1540.0640.1400.2430.0900.0960.1130.054
key-0.004-0.002-0.029-0.003-0.0530.0300.0030.1161.000-0.0540.0430.2730.0140.0200.0690.1350.070-0.004
liveness0.0050.0000.020-0.162-0.0340.109-0.0920.120-0.0541.0000.0440.076-0.0490.0230.0400.074-0.0750.004
loudness-0.355-0.005-0.0450.186-0.1170.692-0.2160.1540.0430.0441.0000.0790.1440.0620.0220.0830.280-0.016
mode0.1210.0160.0120.2000.0760.1420.0570.0640.2730.0760.0791.0000.1190.1270.1130.1060.1290.019
popularity-0.118-0.040-0.133-0.0270.0430.0030.0560.1400.014-0.0490.1440.1191.000-0.1800.0090.084-0.019-0.099
speechiness-0.069-0.0190.0020.331-0.1700.131-0.0630.2430.0200.0230.0620.127-0.1801.0000.0570.1170.085-0.027
tempo-0.098-0.0020.021-0.161-0.0440.1020.0290.0900.0690.0400.0220.1130.0090.0571.0000.5100.029-0.010
time_signature0.1440.0080.0260.2430.0940.1210.0570.0960.1350.0740.0830.1060.0840.1170.5101.0000.1230.020
valence-0.0970.005-0.0590.385-0.2310.340-0.0730.1130.070-0.0750.2800.129-0.0190.0850.0290.1231.000-0.007
weekly_movement0.0110.277-0.176-0.0370.004-0.011-0.0060.054-0.0040.004-0.0160.019-0.099-0.027-0.0100.020-0.0071.000

Missing values

2024-09-24T12:43:55.099416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-24T12:43:59.248731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-24T12:44:06.385519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

spotify_idnameartistsdaily_rankdaily_movementweekly_movementcountrysnapshot_datepopularityis_explicitduration_msalbum_namealbum_release_datedanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signature
02plbrEY59IikOBgBGLjaoeDie With A SmileLady Gaga, Bruno Mars100NaN2024-09-2099False251667Die With A Smile2024-08-160.5210.5926-7.77700.03040.308000.0000000.12200.535157.9693
16dOtVTDdiauQNBQEDOtlABBIRDS OF A FEATHERBillie Eilish200NaN2024-09-20100False210373HIT ME HARD AND SOFT2024-05-170.7470.5072-10.17110.03580.200000.0608000.11700.438104.9784
25G2f63n7IPVPPjfNIGih7QTasteSabrina Carpenter300NaN2024-09-2096False157279Short n' Sweet2024-08-230.6740.9073-4.08610.06400.101000.0000000.29700.721112.9644
37tI8dRuH2Yc6RuoTjxo4dUWhoJimin401NaN2024-09-2094False170887MUSE2024-07-190.6600.7560-3.74300.03200.002890.0000000.19300.838116.0344
42qSkIjg1o9h3YT9RAgYN75EspressoSabrina Carpenter5045NaN2024-09-2097True175459Espresso2024-04-120.7010.7600-5.47810.02850.107000.0000650.18500.690103.9694
50WbMK4wrZ1wFSty9F7FCguGood Luck, Babe!Chappell Roan601NaN2024-09-2097False218423Good Luck, Babe!2024-04-050.7000.58211-5.96000.03560.050200.0000000.08810.785116.7124
65N3hjp1WNayUPZrA8kJmJPPlease Please PleaseSabrina Carpenter7143NaN2024-09-2096True186365Please Please Please2024-06-060.6690.5869-6.07310.05400.274000.0000000.10400.579107.0714
76WatFBLVB0x077xWeoVc2kSi Antes Te Hubiera ConocidoKAROL G810NaN2024-09-2096False195824Si Antes Te Hubiera Conocido2024-06-210.9240.66811-6.79510.04690.446000.0005940.06780.787128.0274
82PnlsTsOTLE5jnBnNe2K0AThe Emptiness MachineLinkin Park9-2-3NaN2024-09-2092True190427The Emptiness Machine2024-09-050.4660.8727-3.34410.03360.015600.0000000.12100.806184.1154
93xkHsmpQCBMytMJNiDf3IiBeautiful ThingsBenson Boone1010NaN2024-09-2091False180304Fireworks & Rollerblades2024-04-050.4720.47110-5.69210.06030.151000.0000000.14000.219105.0293
spotify_idnameartistsdaily_rankdaily_movementweekly_movementcountrysnapshot_datepopularityis_explicitduration_msalbum_namealbum_release_datedanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signature
12231934MjDJD8cW7iVeWInc2BdyjMONACOBad Bunny4190AE2023-10-1891True267194nadie sabe lo que va a pasar mañana2023-10-130.7870.6214-5.00900.06800.15000.0004020.58000.130139.0564
12231943eP13S8D5m2cweMEg3ZDedVirginia BeachDrake4280AE2023-10-1887True251094For All The Dogs2023-10-060.4020.5141-7.32200.04710.10600.0000000.18100.200120.0094
12231957DSAEUvxU8FajXtRloy8M0FlowersMiley Cyrus4370AE2023-10-1890False200600Endless Summer Vacation2023-08-180.7060.6910-4.77510.06330.05840.0000700.02320.632118.0484
12231966g95dK7o7vVh8ZCnDAseU5كلام عينيهSherine4460AE2023-10-1873False235833نساى2018-10-240.5730.7591-4.70600.04280.04770.0000010.19300.745131.9314
12231976I3mqTwhRpn34SLVafSH7GGhostJustin Bieber4550AE2023-10-1888False153190Justice2021-03-190.6010.7412-5.56910.04780.18500.0000290.41500.441153.9604
12231980AYt6NMyyLd0rLuvr0UkMHSlime You Out (feat. SZA)Drake, SZA4640AE2023-10-1884True310490For All The Dogs2023-10-060.4830.4085-9.24300.05020.50800.0000000.25900.10588.8803
12231992Gk6fi0dqt91NKvlzGsmm7SAY MY GRACE (feat. Travis Scott)Offset, Travis Scott4730AE2023-10-1880True173253SET IT OFF2023-10-130.7730.63510-5.06010.04520.05850.0000000.13200.476121.8794
122320026b3oVLrRUaaybJulow9kzPeopleLibianca4820AE2023-10-1888False184791People2022-12-060.5730.42210-7.62100.06780.55100.0000130.10200.693124.3575
12232015ydjxBSUIDn26MFzU3asP4Rainy DaysV4910AE2023-10-1888False179560Layover2023-08-110.6330.4549-8.01600.08750.73900.0000000.14800.28274.8284
122320259NraMJsLaMCVtwXTSia8iPradacassö, RAYE, D-Block Europe5000AE2023-10-1894True132359Prada2023-08-110.6380.7178-5.80410.03750.00100.0000020.11300.422141.9044